Web: http://arxiv.org/abs/2102.06828

June 23, 2022, 1:11 a.m. | Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang

cs.LG updates on arXiv.org arxiv.org

Recently, deep neural networks have gained increasing popularity in the field
of time series forecasting. A primary reason for their success is their ability
to effectively capture complex temporal dynamics across multiple related time
series. The advantages of these deep forecasters only start to emerge in the
presence of a sufficient amount of data. This poses a challenge for typical
forecasting problems in practice, where there is a limited number of time
series or observations per time series, or both. …

arxiv attention domain adaptation forecasting lg time time series time series forecasting

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY